
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.

import argparse
import os
import shutil
from loguru import logger

import tensorrt as trt
import torch
from torch2trt import torch2trt

from yolox.exp import get_exp


def make_parser():
    parser = argparse.ArgumentParser("YOLOX ncnn deploy")
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default=None, help="model name")

    parser.add_argument(
        "-f",
        "--exp_file",
        default=None,
        type=str,
        help="please input your experiment description file",
    )
    parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt path")
    parser.add_argument(
        "-w", '--workspace', type=int, default=32, help='max workspace size in detect'
    )
    parser.add_argument("-b", '--batch', type=int, default=1, help='max batch size in detect')
    return parser


@logger.catch
@torch.no_grad()
def main():
    args = make_parser().parse_args()
    exp = get_exp(args.exp_file, args.name)
    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    model = exp.get_model()
    file_name = os.path.join(exp.output_dir, args.experiment_name)
    os.makedirs(file_name, exist_ok=True)
    if args.ckpt is None:
        ckpt_file = os.path.join(file_name, "best_ckpt.pth")
    else:
        ckpt_file = args.ckpt

    ckpt = torch.load(ckpt_file, map_location="cpu")
    # load the model state dict

    model.load_state_dict(ckpt["model"])
    logger.info("loaded checkpoint done.")
    model.eval()
    model.cuda()
    model.head.decode_in_inference = False
    x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda()
    model_trt = torch2trt(
        model,
        [x],
        fp16_mode=True,
        log_level=trt.Logger.INFO,
        max_workspace_size=(1 << args.workspace),
        max_batch_size=args.batch,
    )
    torch.save(model_trt.state_dict(), os.path.join(file_name, "model_trt.pth"))
    logger.info("Converted TensorRT model done.")
    engine_file = os.path.join(file_name, "model_trt.engine")
    engine_file_demo = os.path.join("demo", "TensorRT", "cpp", "model_trt.engine")
    with open(engine_file, "wb") as f:
        f.write(model_trt.engine.serialize())

    shutil.copyfile(engine_file, engine_file_demo)

    logger.info("Converted TensorRT model engine file is saved for C++ inference.")


if __name__ == "__main__":
    main()
